juhoinkinen (Juho Inkinen)

Annif 1.2 has been released!

https://github.com/NatLibFi/Annif/releases/tag/v1.2.0

This release introduces language detection capabilities in the REST API and CLI, improves 🤗 Hugging Face Hub integration, and also includes the usual maintenance work and minor bug fixes.

The new REST API endpoint /v1/detect-language expects POST requests that contain a JSON object with the text whose language is to be analyzed and a list of candidate languages. Similarly, the CLI has a new command annif detect-language. Annif projects are typically language specific, so a text of a given language needs to be processed with a project intended for that language; the language detection feature can help in this. For details see this [Wiki page](https://github.com/NatLibFi/Annif/wiki/Language-detection). The language detection is performed with the Simplemma library by [@adbar](https://github.com/adbar) et al.

The annif download command has a new --trust-repo option, which needs to be used if the repository to download from has not been used previously (that is if the repository does not appear in the local Hugging Face Hub cache). This option is introduced to raise awareness of the risks of downloading projects from the internet; the project downloads should only be done from trusted sources. For more information see the [Hugging Face Hub documentation](https://huggingface.co/docs/hub/en/security-pickle).

This release also includes automation of downloading the NLTK datapackage used for tokenization to simplify Annif installation. Maintenance tasks include upgrading dependencies, including a new version of Simplemma that allows better control over memory usage. The bug fixes include restoring the --host option of the annif run command.

Python 3.12 is now fully supported (previously NN-ensemble and STWFSA backends were not supported on Python 3.12).

NatLibFi/Annif

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🚀 I'm excited to announce that huggingface_hub's InferenceClient now supports OpenAI's Python client syntax! For developers integrating AI into their codebases, this means you can switch to open-source models with just three lines of code. Here's a quick example of how easy it is.

Why use the InferenceClient?
🔄 Seamless transition: keep your existing code structure while leveraging LLMs hosted on the Hugging Face Hub.
🤗 Direct integration: easily launch a model to run inference using our Inference Endpoint service.
🚀 Stay Updated: always be in sync with the latest Text-Generation-Inference (TGI) updates.

More details in https://huggingface.co/docs/huggingface_hub/main/en/guides/inference#openai-compatibility